A Bayesian Model Averaging Methodology for Detecting EEG Artifacts

In this paper we describe a Bayesian Model Averaging (BMA) methodology developed for detecting artifacts in electroencephalograms (EEGs). The EEGs can be heavily corrupted by cardiac, eye movement, muscle and noise artifacts, so that EEG experts need to automatically detect them with a given level of confidence. In theory, the BMA methodology allows experts to evaluate the confidence in decision making most accurately. However, the non- stationary nature of EEGs makes the use of this methodology difficult. In our experiments with the sleep EEGs, the proposed BMA technique is shown to provide a better performance in terms of predictive accuracy.